Tag Archives: amplitudes

Zoomplitudes Retrospective

During Zoomplitudes (my field’s big yearly conference, this year on Zoom) I didn’t have time to write a long blog post. I said a bit about the format, but didn’t get a chance to talk about the science. I figured this week I’d go back and give a few more of my impressions. As always, conference posts are a bit more technical than my usual posts, so regulars be warned!

The conference opened with a talk by Gavin Salam, there as an ambassador for LHC physics. Salam pointed out that, while a decent proportion of speakers at Amplitudes mention the LHC in their papers, that fraction has fallen over the years. (Another speaker jokingly wondered which of those mentions were just in the paper’s introduction.) He argued that there is still useful work for us, LHC measurements that will require serious amplitudes calculations to understand. He also brought up what seems like the most credible argument for a new, higher-energy collider: that there are important properties of the Higgs, in particular its interactions, that we still have not observed.

The next few talks hopefully warmed Salam’s heart, as they featured calculations for real-world particle physics. Nathaniel Craig and Yael Shadmi in particular covered the link between amplitudes and Standard Model Effective Field Theory (SMEFT), a method to systematically characterize corrections beyond the Standard Model. Shadmi’s talk struck me because the kind of work she described (building the SMEFT “amplitudes-style”, directly from observable information rather than more complicated proxies) is something I’d seen people speculate about for a while, but which hadn’t been done until quite recently. Now, several groups have managed it, and look like they’ve gotten essentially “all the way there”, rather than just partial results that only manage to replicate part of the SMEFT. Overall it’s much faster progress than I would have expected.

After Shadmi’s talk was a brace of talks on N=4 super Yang-Mills, featuring cosmic Galois theory and an impressively groan-worthy “origin story” joke. The final talk of the day, by Hofie Hannesdottir, covered work with some of my colleagues at the NBI. Due to coronavirus I hadn’t gotten to hear about this in person, so it was good to hear a talk on it, a blend of old methods and new priorities to better understand some old discoveries.

The next day focused on a topic that has grown in importance in our community, calculations for gravitational wave telescopes like LIGO. Several speakers focused on new methods for collisions of spinning objects, where a few different approaches are making good progress (Radu Roiban’s proposal to use higher-spin field theory was particularly interesting) but things still aren’t quite “production-ready”. The older, post-Newtonian method is still very much production-ready, as evidenced by Michele Levi’s talk that covered, among other topics, our recent collaboration. Julio Parra-Martinez discussed some interesting behavior shared by both supersymmetric and non-supersymmetric gravity theories. Thibault Damour had previously expressed doubts about use of amplitudes methods to answer this kind of question, and part of Parra-Martinez’s aim was to confirm the calculation with methods Damour would consider more reliable. Damour (who was actually in the audience, which I suspect would not have happened at an in-person conference) had already recanted some related doubts, but it’s not clear to me whether that extended to the results Parra-Martinez discussed (or whether Damour has stated the problem with his old analysis).

There were a few talks that day that didn’t relate to gravitational waves, though this might have been an accident, since both speakers also work on that topic. Zvi Bern’s talk linked to the previous day’s SMEFT discussion, with a calculation using amplitudes methods of direct relevance to SMEFT researchers. Clifford Cheung’s talk proposed a rather strange/fun idea, conformal symmetry in negative dimensions!

Wednesday was “amplituhedron day”, with a variety of talks on positive geometries and cluster algebras. Featured in several talks was “tropicalization“, a mathematical procedure that can simplify complicated geometries while still preserving essential features. Here, it was used to trim down infinite “alphabets” conjectured for some calculations into a finite set, and in doing so understand the origin of “square root letters”. The day ended with a talk by Nima Arkani-Hamed, who despite offering to bet that he could finish his talk within the half-hour slot took almost twice that. The organizers seemed to have planned for this, since there was one fewer talk that day, and as such the day ended at roughly the usual time regardless.

We also took probably the most unique conference photo I will ever appear in.

For lack of a better name, I’ll call Thursday’s theme “celestial”. The day included talks by cosmologists (including approaches using amplitudes-ish methods from Daniel Baumann and Charlotte Sleight, and a curiously un-amplitudes-related talk from Daniel Green), talks on “celestial amplitudes” (amplitudes viewed from the surface of an infinitely distant sphere), and various talks with some link to string theory. I’m including in that last category intersection theory, which has really become its own thing. This included a talk by Simon Caron-Huot about using intersection theory more directly in understanding Feynman integrals, and a talk by Sebastian Mizera using intersection theory to investigate how gravity is Yang-Mills squared. Both gave me a much better idea of the speakers’ goals. In Mizera’s case he’s aiming for something very ambitious. He wants to use intersection theory to figure out when and how one can “double-copy” theories, and might figure out why the procedure “got stuck” at five loops. The day ended with a talk by Pedro Vieira, who gave an extremely lucid and well-presented “blackboard-style” talk on bootstrapping amplitudes.

Friday was a grab-bag of topics. Samuel Abreu discussed an interesting calculation using the numerical unitarity method. It was notable in part because renormalization played a bigger role than it does in most amplitudes work, and in part because they now have a cool logo for their group’s software, Caravel. Claude Duhr and Ruth Britto gave a two-part talk on their work on a Feynman integral coaction. I’d had doubts about the diagrammatic coaction they had worked on in the past because it felt a bit ad-hoc. Now, they’re using intersection theory, and have a clean story that seems to tie everything together. Andrew McLeod talked about our work on a Feynman diagram Calabi-Yau “bestiary”, while Cristian Vergu had a more rigorous understanding of our “traintrack” integrals.

There are two key elements of a conference that are tricky to do on Zoom. You can’t do a conference dinner, so you can’t do the traditional joke-filled conference dinner speech. The end of the conference is also tricky: traditionally, this is when everyone applauds the organizers and the secretaries are given flowers. As chair for the last session, Lance Dixon stepped up to fill both gaps, with a closing speech that was both a touching tribute to the hard work of organizing the conference and a hilarious pile of in-jokes, including a participation award to Arkani-Hamed for his (unprecedented, as far as I’m aware) perfect attendance.

The Sum of Our Efforts

I got a new paper out last week, with Andrew McLeod, Henrik Munch, and Georgios Papathanasiou.

A while back, some collaborators and I found an interesting set of Feynman diagrams that we called “Omega”. These Omega diagrams were fun because they let us avoid one of the biggest limitations of particle physics: that we usually have to compute approximations, diagram by diagram, rather than finding an exact answer. For these Omegas, we figured out how to add all the infinite set of Omega diagrams up together, with no approximation.

One implication of this was that, in principle, we now knew the answer for each individual Omega diagram, far past what had been computed before. However, writing down these answers was easier said than done. After some wrangling, we got the answer for each diagram in terms of an infinite sum. But despite tinkering with it for a while, even our resident infinite sum expert Georgios Papathanasiou couldn’t quite sum them up.

Naturally, this made me think the sums would make a great Master’s project.

When Henrik Munch showed up looking for a project, Andrew McLeod and I gave him several options, but he settled on the infinite sums. Impressively, he ended up solving the problem in two different ways!

First, he found an old paper none of us had seen before, that gave a general method for solving that kind of infinite sum. When he realized that method was really annoying to program, he took the principle behind it, called telescoping, and came up with his own, simpler method, for our particular case.

Picture an old-timey folding telescope. It might be long when fully extended, but when you fold it up each piece fits inside the previous one, resulting in a much smaller object. Telescoping a sum has the same spirit. If each pair of terms in a sum “fit together” (if their difference is simple), you can rearrange them so that most of the difficulty “cancels out” and you’re left with a much simpler sum.

Henrik’s telescoping idea worked even better than expected. We found that we could do, not just the Omega sums, but other sums in particle physics as well. Infinite sums are a very well-studied field, so it was interesting to find something genuinely new.

The rest of us worked to generalize the result, to check the examples and to put it in context. But the core of the work was Henrik’s. I’m really proud of what he accomplished. If you’re looking for a PhD student, he’s on the market!

Zoomplitudes 2020

This week, I’m at Zoomplitudes!

My field’s big yearly conference, Amplitudes, was supposed to happen in Michigan this year, but with the coronavirus pandemic it was quickly clear that would be impossible. Luckily, Anastasia Volovich stepped in to Zoomganize the conference from Brown.

Obligatory photo of the conference venue

The conference is still going, so I’ll say more about the scientific content later. (Except to say there have been a lot of interesting talks!) Here, I’ll just write a bit about the novel experience of going to a conference on Zoom.

Time zones are always tricky in an online conference like this. Our field is spread widely around the world, but not evenly: there are a few areas with quite a lot of amplitudes research. As a result, Zoomganizing from the US east coast seems like it was genuinely the best compromise. It means the talks start a bit early for the west coast US (6am their time), but still end not too late for the Europeans (10:30pm CET). The timing is awkward for our colleagues in China and Taiwan, but they can still join in the morning session (their evening). Overall, I don’t think it was possible to do better there.

Usually, Amplitudes is accompanied by a one-week school for Master’s and PhD students. That wasn’t feasible this year, but to fill the gap Nima Arkani-Hamed gave a livestreamed lecture the Friday before, which apparently clocked in at thirteen hours!

One aspect of the conference that really impressed me was the Slack space. The organizers wanted to replicate the “halls” part of the conference, with small groups chatting around blackboards between the talks. They set up a space on the platform Slack, and let attendees send private messages and make their own channels for specific topics. Soon the space was filled with lively discussion, including a #coffeebreak channel with pictures of everyone’s morning coffee. I think the organizers did a really good job of achieving the kind of “serendipity” I talked about in this post, where accidental meetings spark new ideas. More than that, this is the kind of thing I’d appreciate even in face-to-face conferences. The ability to message anyone at the conference from a shared platform, to have discussions that anyone can stumble on and read later, to post papers and links, all of this seems genuinely quite useful. As one of the organizers for Amplitudes 2021, I may soon get a chance to try this out.

Zoom itself worked reasonably well. A few people had trouble connecting or sharing screens, but overall things worked reliably, and the Zoom chat window is arguably better than people whispering to each other in the back of an in-person conference. One feature of the platform that confused people a bit is that co-hosts can’t raise their hands to ask questions: since speakers had to be made co-hosts to share their screens they had a harder time asking questions during other speakers’ talks.

A part I was more frustrated by was the scheduling. Fitting everyone who wanted to speak between 6am west coast and 10:30pm Europe must have been challenging, and the result was a tightly plotted conference, with three breaks each no more than 45 minutes. That’s already a bit tight, but it ended up much tighter because most talks went long. The conference’s 30 minute slots regularly took 40 minutes, between speakers running over and questions going late. As a result, the conference’s “lunch break” (roughly dinner break for the Europeans) was often only 15 minutes. I appreciate the desire for lively discussion, especially since the conference is recorded and the question sessions can be a resource for others. But I worry that, as a pitfall of remote conferences, the inconveniences people suffer to attend can become largely invisible. Yes, we can always skip a talk, and watch the recording later. Yes, we can prepare food beforehand. Still, I don’t think a 15 minute lunch break was what the organizers had in mind, and if our community does more remote conferences we should brainstorm ways to avoid this problem next time.

I’m curious how other fields are doing remote conferences right now. Even after the pandemic, I suspect some fields will experiment with this kind of thing. It’s worth sharing and paying attention to what works and what doesn’t.

4gravitons, Spinning Up

I had a new paper out last week, with Michèle Levi and Andrew McLeod. But to explain it, I’ll need to clarify something about our last paper.

Two weeks ago, I told you that Andrew and Michèle and I had written a paper, predicting what gravitational wave telescopes like LIGO see when black holes collide. You may remember that LIGO doesn’t just see colliding black holes: it sees colliding neutron stars too. So why didn’t we predict what happens when neutron stars collide?

Actually, we did. Our calculation doesn’t just apply to black holes. It applies to neutron stars too. And not just neutron stars: it applies to anything of roughly the right size and shape. Black holes, neutron stars, very large grapefruits…

LIGO’s next big discovery

That’s the magic of Effective Field Theory, the “zoom lens” of particle physics. Zoom out far enough, and any big, round object starts looking like a particle. Black holes, neutron stars, grapefruits, we can describe them all using the same math.

Ok, so we can describe both black holes and neutron stars. Can we tell the difference between them?

In our last calculation, no. In this one, yes!

Effective Field Theory isn’t just a zoom lens, it’s a controlled approximation. That means that when we “zoom out” we don’t just throw out anything “too small to see”. Instead, we approximate it, estimating how big of an effect it can have. Depending on how precise we want to be, we can include more and more of these approximated effects. If our estimates are good, we’ll include everything that matters, and get a good approximation for what we’re trying to observe.

At the precision of our last calculation, a black hole and a neutron star still look exactly the same. Our new calculation aims for a bit higher precision though. (For the experts: we’re at a higher order in spin.) The higher precision means that we can actually see the difference: our result changes for two colliding black holes versus two colliding grapefruits.

So does that mean I can tell you what happens when two neutron stars collide, according to our calculation? Actually, no. That’s not because we screwed up the calculation: it’s because some of the properties of neutron stars are unknown.

The Effective Field Theory of neutron stars has what we call “free parameters”, unknown variables. People have tried to estimate some of these (called “Love numbers” after the mathematician A. E. H. Love), but they depend on the details of how neutron stars work: what stuff they contain, how that stuff is shaped, and how it can move. To find them out, we probably can’t just calculate: we’ll have to measure, observe an actual neutron star collision and see what the numbers actually are.

That’s one of the purposes of gravitational wave telescopes. It’s not (as far as I know) something LIGO can measure. But future telescopes, with more precision, should be able to. By watching two colliding neutron stars and comparing to a high-precision calculation, physicists will better understand what those neutron stars are made of. In order to do that, they will need someone to do that high-precision calculation. And that’s why people like me are involved.

4gravitons Exchanges a Graviton

I had a new paper up last Friday with Michèle Levi and Andrew McLeod, on a topic I hadn’t worked on before: colliding black holes.

I am an “amplitudeologist”. I work on particle physics calculations, computing “scattering amplitudes” to find the probability that fundamental particles bounce off each other. This sounds like the farthest thing possible from black holes. Nevertheless, the two are tightly linked, through the magic of something called Effective Field Theory.

Effective Field Theory is a kind of “zoom knob” for particle physics. You “zoom out” to some chosen scale, and write down a theory that describes physics at that scale. Your theory won’t be a complete description: you’re ignoring everything that’s “too small to see”. It will, however, be an effective description: one that, at the scale you’re interested in, is effectively true.

Particle physicists usually use Effective Field Theory to go between different theories of particle physics, to zoom out from strings to quarks to protons and neutrons. But you can zoom out even further, all the way out to astronomical distances. Zoom out far enough, and even something as massive as a black hole looks like just another particle.

Just click the “zoom X10” button fifteen times, and you’re there!

In this picture, the force of gravity between black holes looks like particles (specifically, gravitons) going back and forth. With this picture, physicists can calculate what happens when two black holes collide with each other, making predictions that can be checked with new gravitational wave telescopes like LIGO.

Researchers have pushed this technique quite far. As the calculations get more and more precise (more and more “loops”), they have gotten more and more challenging. This is particularly true when the black holes are spinning, an extra wrinkle in the calculation that adds a surprising amount of complexity.

That’s where I came in. I can’t compete with the experts on black holes, but I certainly know a thing or two about complicated particle physics calculations. Amplitudeologists, like Andrew McLeod and me, have a grab-bag of tricks that make these kinds of calculations a lot easier. With Michèle Levi’s expertise working with spinning black holes in Effective Field Theory, we were able to combine our knowledge to push beyond the state of the art, to a new level of precision.

This project has been quite exciting for me, for a number of reasons. For one, it’s my first time working with gravitons: despite this blog’s name, I’d never published a paper on gravity before. For another, as my brother quipped when he heard about it, this is by far the most “applied” paper I’ve ever written. I mostly work with a theory called N=4 super Yang-Mills, a toy model we use to develop new techniques. This paper isn’t a toy model: the calculation we did should describe black holes out there in the sky, in the real world. There’s a decent chance someone will use this calculation to compare with actual data, from LIGO or a future telescope. That, in particular, is an absurdly exciting prospect.

Because this was such an applied calculation, it was an opportunity to explore the more applied part of my own field. We ended up using well-known techniques from that corner, but I look forward to doing something more inventive in future.

You Can’t Anticipate a Breakthrough

As a scientist, you’re surrounded by puzzles. For every test and every answer, ten new questions pop up. You can spend a lifetime on question after question, never getting bored.

But which questions matter? If you want to change the world, if you want to discover something deep, which questions should you focus on? And which should you ignore?

Last year, my collaborators and I completed a long, complicated project. We were calculating the chance fundamental particles bounce off each other in a toy model of nuclear forces, pushing to very high levels of precision. We managed to figure out a lot, but as always, there were many questions left unanswered in the end.

The deepest of these questions came from number theory. We had noticed surprising patterns in the numbers that showed up in our calculation, reminiscent of the fancifully-named Cosmic Galois Theory. Certain kinds of numbers never showed up, while others appeared again and again. In order to see these patterns, though, we needed an unusual fudge factor: an unexplained number multiplying our result. It was clear that there was some principle at work, a part of the physics intimately tied to particular types of numbers.

There were also questions that seemed less deep. In order to compute our result, we compared to predictions from other methods: specific situations where the question becomes simpler and there are other ways of calculating the answer. As we finished writing the paper, we realized we could do more with some of these predictions. There were situations we didn’t use that nonetheless simplified things, and more predictions that it looked like we could make. By the time we saw these, we were quite close to publishing, so most of us didn’t have the patience to follow these new leads. We just wanted to get the paper out.

At the time, I expected the new predictions would lead, at best, to more efficiency. Maybe we could have gotten our result faster, or cleaned it up a bit. They didn’t seem essential, and they didn’t seem deep.

Fast forward to this year, and some of my collaborators (specifically, Lance Dixon and Georgios Papathanasiou, along with Benjamin Basso) have a new paper up: “The Origin of the Six-Gluon Amplitude in Planar N=4 SYM”. The “origin” in their title refers to one of those situations: when the variables in the problem are small, and you’re close to the “origin” of a plot in those variables. But the paper also sheds light on the origin of our calculation’s mysterious “Cosmic Galois” behavior.

It turns out that the origin (of the plot) can be related to another situation, when the paths of two particles in our calculation almost line up. There, the calculation can be done with another method, called the Pentagon Operator Product Expansion, or POPE. By relating the two, Basso, Dixon, and Papathanasiou were able to predict not only how our result should have behaved near the origin, but how more complicated as-yet un-calculated results should behave.

The biggest surprise, though, lurked in the details. Building their predictions from the POPE method, they found their calculation separated into two pieces: one which described the physics of the particles involved, and a “normalization”. This normalization, predicted by the POPE method, involved some rather special numbers…the same as the “fudge factor” we had introduced earlier! Somehow, the POPE’s physics-based setup “knows” about Cosmic Galois Theory!

It seems that, by studying predictions in this specific situation, Basso, Dixon, and Papathanasiou have accomplished something much deeper: a strong hint of where our mysterious number patterns come from. It’s rather humbling to realize that, were I in their place, I never would have found this: I had assumed “the origin” was just a leftover detail, perhaps useful but not deep.

I’m still digesting their result. For now, it’s a reminder that I shouldn’t try to pre-judge questions. If you want to learn something deep, it isn’t enough to sit thinking about it, just focusing on that one problem. You have to follow every lead you have, work on every problem you can, do solid calculation after solid calculation. Sometimes, you’ll just make incremental progress, just fill in the details. But occasionally, you’ll have a breakthrough, something that justifies the whole adventure and opens your door to something strange and new. And I’m starting to think that when it comes to breakthroughs, that’s always been the only way there.

Why You Might Want to Bootstrap

A few weeks back, Quanta Magazine had an article about attempts to “bootstrap” the laws of physics, starting from simple physical principles and pulling out a full theory “by its own bootstraps”. This kind of work is a cornerstone of my field, a shared philosophy that motivates a lot of what we do. Building on deep older results, people in my field have found that just a few simple principles are enough to pick out specific physical theories.

There are limits to this. These principles pick out broad traits of theories: gravity versus the strong force versus the Higgs boson. As far as we know they don’t separate more closely related forces, like the strong nuclear force and the weak nuclear force. (Originally, the Quanta article accidentally made it sound like we know why there are four fundamental forces: we don’t, and the article’s phrasing was corrected.) More generally, a bootstrap method isn’t going to tell you which principles are the right ones. For any set of principles, you can always ask “why?”

With that in mind, why would you want to bootstrap?

First, it can make your life simpler. Those simple physical principles may be clear at the end, but they aren’t always obvious at the start of a calculation. If you don’t make good use of them, you might find you’re calculating many things that violate those principles, things that in the end all add up to zero. Bootstrapping can let you skip that part of the calculation, and sometimes go straight to the answer.

Second, it can suggest possibilities you hadn’t considered. Sometimes, your simple physical principles don’t select a unique theory. Some of the options will be theories you’ve heard of, but some might be theories that never would have come up, or even theories that are entirely new. Trying to understand the new theories, to see whether they make sense and are useful, can lead to discovering new principles as well.

Finally, even if you don’t know which principles are the right ones, some principles are better than others. If there is an ultimate theory that describes the real world, it can’t be logically inconsistent. That’s a start, but it’s quite a weak requirement. There are principles that aren’t required by logic itself, but that still seem important in making the world “make sense”. Often, we appreciate these principles only after we’ve seen them at work in the real world. The best example I can think of is relativity: while Newtonian mechanics is logically consistent, it requires a preferred reference frame, a fixed notion for which things are moving and which things are still. This seemed reasonable for a long time, but now that we understand relativity the idea of a preferred reference frame seems like it should have been obviously wrong. It introduces something arbitrary into the laws of the universe, a “why is it that way?” question that doesn’t have an answer. That doesn’t mean it’s logically inconsistent, or impossible, but it does make it suspect in a way other ideas aren’t. Part of the hope of these kinds of bootstrap methods is that they uncover principles like that, principles that aren’t mandatory but that are still in some sense “obvious”. Hopefully, enough principles like that really do specify the laws of physics. And if they don’t, we’ll at least have learned how to calculate better.

Calculating the Hard Way, for Science!

I had a new paper out last week, with Jacob Bourjaily and Matthias Volk. We’re calculating the probability that particles bounce off each other in our favorite toy model, N=4 super Yang-Mills. And this time, we’re doing it the hard way.

The “easy way” we didn’t take is one I have a lot of experience with. Almost as long as I’ve been writing this blog, I’ve been calculating these particle probabilities by “guesswork”: starting with a plausible answer, then honing it down until I can be confident it’s right. This might sound reckless, but it works remarkably well, letting us calculate things we could never have hoped for with other methods. The catch is that “guessing” is much easier when we know what we’re looking for: in particular, it works much better in toy models than in the real world.

Over the last few years, though, I’ve been using a much more “normal” method, one that so far has a better track record in the real world. This method, too, works better than you would expect, and we’ve managed some quite complicated calculations.

So we have an “easy way”, and a “hard way”. Which one is better? Is the hard way actually harder?

To test that, you need to do the same calculation both ways, and see which is easier. You want it to be a fair test: if “guessing” only works in the toy model, then you should do the “hard” version in the toy model as well. And you don’t want to give “guessing” any unfair advantages. In particular, the “guess” method works best when we know a lot about the result we’re looking for: what it’s made of, what symmetries it has. In order to do a fair test, we must use that knowledge to its fullest to improve the “hard way” as well.

We picked an example in the middle: not too easy, and not too hard, a calculation that was done a few years back “the easy way” but not yet done “the hard way”. We plugged in all the modern tricks we could, trying to use as much of what we knew as possible. We trained a grad student: Matthias Volk, who did the lion’s share of the calculation and learned a lot in the process. We worked through the calculation, and did it properly the hard way.

Which method won?

In the end, the hard way was indeed harder…but not by that much! Most of the calculation went quite smoothly, with only a few difficulties at the end. Just five years ago, when the calculation was done “the easy way”, I doubt anyone would have expected the hard way to be viable. But with modern tricks it wasn’t actually that hard.

This is encouraging. It tells us that the “hard way” has potential, that it’s almost good enough to compete at this kind of calculation. It tells us that the “easy way” is still quite powerful. And it reminds us that the more we know, and the more we apply our knowledge, the more we can do.

QCD Meets Gravity 2019

I’m at UCLA this week for QCD Meets Gravity, a conference about the surprising ways that gravity is “QCD squared”.

When I attended this conference two years ago, the community was branching out into a new direction: using tools from particle physics to understand the gravitational waves observed at LIGO.

At this year’s conference, gravitational waves have grown from a promising new direction to a large fraction of the talks. While there were still the usual talks about quantum field theory and string theory (everything from bootstrap methods to a surprising application of double field theory), gravitational waves have clearly become a major focus of this community.

This was highlighted before the first talk, when Zvi Bern brought up a recent paper by Thibault Damour. Bern and collaborators had recently used particle physics methods to push beyond the state of the art in gravitational wave calculations. Damour, an expert in the older methods, claims that Bern et al’s result is wrong, and in doing so also questions an earlier result by Amati, Ciafaloni, and Veneziano. More than that, Damour argued that the whole approach of using these kinds of particle physics tools for gravitational waves is misguided.

There was a lot of good-natured ribbing of Damour in the rest of the conference, as well as some serious attempts to confront his points. Damour’s argument so far is somewhat indirect, so there is hope that a more direct calculation (which Damour is currently pursuing) will resolve the matter. In the meantime, Julio Parra-Martinez described a reproduction of the older Amati/Ciafaloni/Veneziano result with more Damour-approved techniques, as well as additional indirect arguments that Bern et al got things right.

Before the QCD Meets Gravity community worked on gravitational waves, other groups had already built a strong track record in the area. One encouraging thing about this conference was how much the two communities are talking to each other. Several speakers came from the older community, and there were a lot of references in both groups’ talks to the other group’s work. This, more than even the content of the talks, felt like the strongest sign that something productive is happening here.

Many talks began by trying to motivate these gravitational calculations, usually to address the mysteries of astrophysics. Two talks were more direct, with Ramy Brustein and Pierre Vanhove speculating about new fundamental physics that could be uncovered by these calculations. I’m not the kind of physicist who does this kind of speculation, and I confess both talks struck me as rather strange. Vanhove in particular explicitly rejects the popular criterion of “naturalness”, making me wonder if his work is the kind of thing critics of naturalness have in mind.

Rooting out the Answer

I have a new paper out today, with Jacob Bourjaily, Andrew McLeod, Matthias Wilhelm, Cristian Vergu and Matthias Volk.

There’s a story I’ve told before on this blog, about a kind of “alphabet” for particle physics predictions. When we try to make a prediction in particle physics, we need to do complicated integrals. Sometimes, these integrals simplify dramatically, in unexpected ways. It turns out we can understand these simplifications by writing the integrals in a sort of “alphabet”, breaking complicated mathematical “periods” into familiar logarithms. If we want to simplify an integral, we can use relations between logarithms like these:

\log(a b)=\log(a)+\log(b),\quad \log(a^n)=n\log(a)

to factor our “alphabet” into pieces as simple as possible.

The simpler the alphabet, the more progress you can make. And in the nice toy model theory we’re working with, the alphabets so far have been simple in one key way. Expressed in the right variables, they’re rational. For example, they contain no square roots.

Would that keep going? Would we keep finding rational alphabets? Or might the alphabets, instead, have square roots?

After some searching, we found a clean test case. There was a calculation we could do with just two Feynman diagrams. All we had to do was subtract one from the other. If they still had square roots in their alphabet, we’d have proven that the nice, rational alphabets eventually had to stop.


So we calculated these diagrams, doing the complicated integrals. And we found they did indeed have square roots in their alphabet, in fact many more than expected. They even had square roots of square roots!

You’d think that would be the end of the story. But square roots are trickier than you’d expect.

Remember that to simplify these integrals, we break them up into an alphabet, and factor the alphabet. What happens when we try to do that with an alphabet that has square roots?

Suppose we have letters in our alphabet with \sqrt{-5}. Suppose another letter is the number 9. You might want to factor it like this:

9=3\times 3

Simple, right? But what if instead you did this:

9=(2+ \sqrt{-5} )\times(2- \sqrt{-5} )

Once you allow \sqrt{-5} in the game, you can factor 9 in two different ways. The central assumption, that you can always just factor your alphabet, breaks down. In mathematical terms, you no longer have a unique factorization domain.

Instead, we had to get a lot more mathematically sophisticated, factoring into something called prime ideals. We got that working and started crunching through the square roots in our alphabet. Things simplified beautifully: we started with a result that was ten million terms long, and reduced it to just five thousand. And at the end of the day, after subtracting one integral from the other…

We found no square roots!

After all of our simplifications, all the letters we found were rational. Our nice test case turned out much, much simpler than we expected.

It’s been a long road on this calculation, with a lot of false starts. We were kind of hoping to be the first to find square root letters in these alphabets; instead it looks like another group will beat us to the punch. But we developed a lot of interesting tricks along the way, and we thought it would be good to publish our “null result”. As always in our field, sometimes surprising simplifications are just around the corner.